Biomorphic rhythmic movement controller

ABSTRACT

An artificial Central Pattern Generator (CPG) based on the naturally-occuring central pattern generator locomotor controller for walking, running, swimming, and flying animals may be constructed to be self-adaptive, by providing for the artificial CPG, which may be a chip, to tune its behavior based on sensory feedback. It is believed that this is the first instance of an adaptive CPG chip. Such a sensory feedback-using system with an artificial CPG may be used in mechanical applications such as a running robotic leg, in walking, flying and swimming machines, and in miniature and larger robots, and also in biological systems, such as a surrogate neural system for patients with spinal damage.

RELATED APPLICATION

[0001] This is a PCT application claiming priority based on U.S.application No. 60/201,748 filed May 4, 2000, which is incorporated byreference herein.

STATEMENT REGARDING GOVERNMENT FUNDING

[0002] This invention was made under NSF grant number 9896362. TheGovernment may have certain rights in this invention.

FIELD OF THE INVENTION

[0003] The invention generally relates to robotics and movement control,and more particularly, to rhythmic movement control systems that aresensitive and adaptive to the environment in which they are used.

BACKGROUND OF THE INVENTION

[0004] Basic rhythmic movements in animals are generated by a network ofneurons in the spinal cord called the Central Pattern Generator (“CPG”).Walking, running, swimming, and flying animals have a biologicallocomotor controller system based on a CPG, which is an autonomousneural circuit generating sustained oscillations needed for locomotion.Naturally-occurring CPGs have been studied and are beginning to beunderstood. Scientists have studied these naturally-occurring biologicalCPG systems and, in the early 1900s, articulated the basic notion ofsuch an oscillation-generating autonomous neural circuit for locomotion.T. G. Brown, “On the nature of the fundamental activity of the nervouscentres; together with an analysis of the conditioning of the rhythmicactivity in progression, and a theory of the evolution of function inthe nervous system,” J. Physiol., vol. 48, pp. 18-46 (1914).

[0005] An autonomous system of neurons can generate a rhythmic patternof neuronal discharge that can drive muscles in a fashion similar tothat seen during normal locomotion. Locomotor CPGs are autonomous inthat they can operate without input from higher centers or from sensors.Under normal conditions, however, these CPGs make extensive use ofsensory feedback from the muscles and skin, as well as descending input.A. H. Cohen, S. Rossignol and S. Grillner, Neural Control of RhythmicMovements in Vertebrates (Wiley & Sons, 1988). Furthermore, the CPGtransmits information upward to modulate higher centers as well as tothe periphery to modulate incoming sensory information.

[0006] The CPG is most often thought of as a collection of distributedelements. For example, in the lamprey (a relatively simple fish-likeanimal), small, isolated portions of the spinal cord can generatesustained oscillations. When the spinal cord is intact, these smallelements coordinate their patterns of activity with their neighbors andover long distances.

[0007] It is well known that sensory input can modulate the activity ofnaturally-occurring, biological CPGs. Modulation of the CPG by sensoryinput can be seen quite clearly in the adjusting of the phase of theCPG. For example, as a walking cat pushes its leg back, sensors in theleg muscles detect stretching. These sensors (called stretch receptors)signal this stretch to the nervous system. Their firing initiates thenext phase of the CPG causing the leg to transition from stance to swingphase.

[0008] After some study of naturally-occurring biological CPG systems,scientists modeled CPGs as systems of coupled non-linear oscillators. Inthe early 1980s Cohen and colleagues introduced a model of the lampreyCPG using a system of phase-coupled oscillators. A. H. Cohen, P. J.Holmes and R. H. Rand, “The nature of the coupling between segmentaloscillators of the lamprey spinal generator for locomotion: Amathematical model,” J. Math. Biol., vol. 13, pp. 345-369 (1982). Later,a model called Adaptive Ring Rules (ARR) based on ideas in Cohen etal.'s and related work was extended for use in robot control. M. A.Lewis, Self-organization of Locomotory Controllers in Robots andAnimals, Ph.D. dissertation, Dept. of Electrical Engineering, Univ. ofSouthern Calif., Los Angeles (1996). An ARR is a model of a non-linearoscillator at a behavioral level. Complex enough to drive a robot, anARR model also allows relatively easy implementation of learning rules.

[0009] Certain conventional non-biological (i.e., modeled) CPG-typechips and circuits have been developed. For example, Still reported on aVLSI implementation similar to a CPG circuit used to drive a smallrobot. S. Still, Presentation at Neurobots Workshop, NIPS*87,Breckenridge, Colo., U.S.A. (1998); S. Still and M. W. Tilden,“Controller for a four legged walking machine, in Neuromorphic Systems:Engineering Silicon from Neurobiology, Eds: L. S. Smith and A. Hamilton(World Scientific, Singapore), pp. 138-148 (1998). Still et al.'scircuit captured some basic ideas of a CPG, and Still's groupdemonstrated rudimentary control of a walking machine. However, Still'ssystem has no motoneuron output stage, and cannot respond to or adaptbased on sensory input.

[0010] Ryckebusch and colleagues created a VLSI CPG chip based onobservations in the thoracic circuits controlling locomotion in locusts.S. Ryckebusch, M. Wehr, and G. Laurent, “Distinct Rhythmic LocomotorPatterns Can Be Generated by a Simple Adaptive Neural Circuit: Biology,Simulation and VLSI Implementation,” J. of Comp. Neuro., vol. 1, pp.339-358 (1994). The resulting VLSI chip was used as a fast simulationtool to explore understanding of the biological system. Their system isnot a robotic system and cannot respond to or use feedback from sensors.

[0011] DeWeerth and colleagues have captured certain neural dynamics ona detailed level. G. Patel, J. Holleman, and S. DeWeerth, “Analog VLSIModel of Intersegmental Coordination with Nearest-Neighbor Coupling,” inAdv. Neural Information Processing, vol. 10, pp. 719-725 (1998). Thissystem of DeWeerth's cannot easily be applied to control a robot,primarily because parameter sensitivity makes such circuits difficult totune. To address this difficulty, DeWeerth et al. more recently haveimplemented neurons that self-adapt their firing-rate. M. Simoni, and S.DeWeerth, “Adaptation in a VLSI Model of a Neuron,” in: Trans. Circuitsand Systems II, vol. 46, no. 7, pp. 967-970 (1999). The adapted DeWeerthsystem, however, is not adaptive and does not use external inputs fromsensors.

[0012] U.S. Pat. No. 5,124,918, entitled “Neural-based autonomousrobotic system” to Beer et al., teaches a system for controlling awalking robot using a rhythmic signal used to coordinate locomotion withmultiple legs. Beer et al.'s neural-based approach, using software, isrelatively basic and does not teach, for example, VLSI implementation,self-adaptation to an environment, low-power compact implementationusing one chip, or silicon learning.

[0013] In recent years, robotics has been developing in many aspects, ofwhich some have been mentioned above. Also, other challenges have beenidentified and are being studied in robotics, such as theminiaturization of walking, running, and flying robots, increasing thereal-time adaptability of robots to the environment, and the creation ofmass-market consumer devices. With these new robotics technologies comedemands for smaller, lower-cost, more power-efficient, more adaptivecontrollers, and, correspondingly, computational support.

[0014] Robotics has largely relied for computational support onmicroprocessor-based technology. Such systems have limitations, such asbeing unable to provide self-adaptive features.

[0015] Not necessarily connected with robotics technologies, a field ofneuromorphic engineering developed. Neuromorphic engineering usesprinciples of biological information processing to address real-worldproblems, constructing neuromorphic systems from silicon, the physics ofwhich in many ways resembles the biophysics of the nervous system.Neuromorphic engineering to date mostly has concentrated on sensoryprocessing, for example, the construction of silicon retinas or siliconcochleas.

[0016] Thus, interesting and exciting advances have been made in thethus-far relatively separate technologies of robotics and neuromorphicengineering. However, work remains to be done to practically bringtogether these technologies. Conventional robotics and related systemsare non-adaptive to their environments, and theoretically the potentialexists for huge advances in the direction of increased adaptiveness tothe environment. Advanced robotics systems, control of mechanical limbs,control of biological limbs, rhythmic movement in biological systems,have been desired, such as increased responsiveness and relatedness tothe environment.

SUMMARY OF THE INVENTION

[0017] After investigating and considering how to make a modeled,non-biological CPG self-adapt, the present inventors arrived at thisinvention, in which a non-biological CPG tunes itself with sensoryfeedback The present invention provides miniature and non-miniaturerobotics systems, modeled non-biological systems that may be used inbiological applications, chips, movement machines, and other systemsthat receive sensory input from, and are adaptive to, an environment inwhich they are operating. For example, the present invention applies ARRtheory in designing such a man-made CPG chip. (Man-made CPG chips,systems and the like are referred to herein as “non-biological” and/or“CPG-based”.) It is a basic object of the present invention to constructnon-biological systems mimicking features of CPGs that occur in nature.(“Biological CPG” is used herein as a shortened way to refer to a CPGthat occurs in nature.)

[0018] It is a further object of the present invention to elucidatepractical applications in which non-biological CPG systems may be used.Examples of applications for non-biological CPG systems include use inbiological systems (such as the human body) as well as non-biologicalsystems (such as robots, movement machines, breathing controllers, andthe like). It is a further object of the present invention to providefor controlling one or more mechanical limbs via a CPG-based system, andto provide for controlling rhythmic movement in a biological system viaa CPG-based system.

[0019] A further object of the invention is to elucidate ways in which amodeled CPG system may be made adaptive and responsive to theenvironment in which it is used. As an important example, the inventionprovides advanced robotics systems and autonomous movement devices(including breathing controllers, running devices, swimming devices,flying devices, and other devices) with sophisticated responsiveness andadaptability to the environments in which they are used.

[0020] In order to accomplish these and other objects of the invention,the present invention in a preferred embodiment provides a CPG-basedsystem, such as a CPG-based system for controlling at least onemechanical limb, comprising at least one mechanical limb and anon-biological CPG that generates commands for controlling the at leastone mechanical limb wherein commands are a function of sensory feedback.The invention provides a CPG-based system for controlling a biologicalsystem for rhythmic movement, comprising: (1) an interface with abiological system that can provide sensory feedback from said biologicalsystem, and (2) a non-biological CPG that generates commands forcontrolling the biological system wherein commands are a function ofsensory feedback.

[0021] In a particularly preferred embodiment, the invention providesfor the CPG-based system to include a system for phase adjustment of theCPG based on a sensory trigger in or derived from sensory feedback. Inanother particularly preferred embodiment, the CPG-based system mayinclude a system for phase adjustment of the central pattern generatorbased on at least one sensory trigger in or derived from sensoryfeedback; and a system for controlling firing frequency of motoneuronsas a function of the sensory feedback or the sensory trigger.

[0022] In a further embodiment, the invention provides for the CPG-basedsystem to include at least one memory device. In a preferred embodimentof the invention, the memory device controls adaptation of output fromthe CPG. In a preferred embodiment of the invention, the output includesintegrate-and-fire neurons.

[0023] The invention also provides for another preferred embodiment inwhich the CPG-based system is at least one chip, and in anotherpreferred embodiment, multiple chips. In a particularly preferredembodiment, the inventive chip contains electronic analogues ofbiological neurons, synapses and time-constraints. In anotherparticularly preferred embodiment, the inventive chip includes dynamicmemories and phase modulators. Another preferred embodiment provided bythe invention is a system including at least one chip in whichcomponents are integrated with hardwired or programmable circuits.

[0024] The invention in another preferred embodiment provides for theCPG-based system to be a non-linear oscillator including electronicanalogues of biological neurons, synapses and time-constraints, dynamicmemories and phase modulators. The invention in a preferred embodimentprovides a CPG-based system wherein the CPG is a distributed system ofat least two non-linear oscillators. In a further inventive embodiment,the invention provides for the distributed system to include at leastone neuron phasically coupled to a neuron or a sensory input. In anotherinventive embodiment, the distributed system includes at least twoneurons phasically coupled to each other, to another neuron, or to asensory input. The invention provides in another embodiment for phasiccoupling that is in-phase, 180 degrees out of phase, or any number ofdegrees out of phase. In a particularly preferred embodiment, theinvention provides phasic coupling based on rhythmic movementapplication. In an especially preferred embodiment, the inventionprovides for including a phase control circuit. Where phasically couplednuerons are used, the invention provides in another embodiment forincluding at least one integrate-and-fire spiking motoneuron driven bythe phasically coupled neurons.

[0025] The invention also provides in another embodiment for includingat least one muscle in the CPG-based system.

[0026] A particularly preferred embodiment of the present inventionprovides a robot.

[0027] The invention in a further embodiment provides for the CPG-basedsystem to include a CPG chip and at least one biological neuron. Theinvention also provides an embodiment in which a CPG-based systemincludes at least one sensor for collecting sensory feedback. In aparticularly preferred embodiment, the CPG-based system includes asystem for phase adjustment of the central pattern generator based on atleast one sensory trigger in the received sensory feedback. Theinvention provides an embodiment in which sensory feedback is receivedfrom a mechanical limb or from a biological limb. The invention alsoprovides an embodiment wherein the sensory feedback is received from asensing modality.

[0028] The invention also provides methods for controlling a mechanicalor biological system for rhythmic movement, such as methods comprising:(A) measuring sensory feedback to obtain measured sensory feedback; (B)processing the measured sensory feedback to obtain data for a pluralityof designated parameters; and (C) via a CPG-based system, applying a setof rules to the obtained data to generate at least one signal forcommanding the limb or biological system for rhythmic movement, whereinthe CPG-based system comprises a circuit that mimics a biological CPG.In a preferred embodiment, such an inventive method includes, via theCPG-based system, applying the generated signal to command the limb orbiological system for rhythmic movement. The invention also provides fora method wherein the CPG system comprises a circuit comprising at leasttwo coupled non-linear oscillators.

[0029] The invention also provides for further embodiments that arerobotics systems, such as a robotics system comprising: (a) a CPG-basedsystem that mimics a biological central pattern generator; and (b) atleast one sensory device. The invention also provides that in aparticularly preferred embodiment of the robotics system, the CPG-basedsystem receives sensory input from the at least one sensory device.

[0030] The invention also provides autonomous movement devices, such asan autonomous movement device for providing rhythmic control, whereinthe autonomous device comprise a non-biological CPG that generatesrhythmic control commands wherein commands are a function of sensoryfeedback. The invention in a further embodiment provides for theautonomous movement device to include at least one mechanical limb. Inanother embodiment, the invention provides that the limb is a leg, arm,wing or appendage for swimming. In some embodiments of the invention, atleast two limbs are included. The invention in other embodimentsprovides a breathing controller, a pacemaker, and a running device.

[0031] The invention also provides a non-biological CPG comprising amemory device; and a system for manipulating neural phasicrelationships.

[0032] Further, the invention provides a method for modifying acontinuous waveform provided by a non-biological CPG, comprising thesteps of: (A) provision of a continuous waveform by a non-biologicalCPG; (B) provision of sensory feedback to the non-biological CPG; (C)rule-application by the non-biological CPG to the sensory feedback; (D)based on the rule-application, determination by the non-biological CPGto modify or maintain the continuous wave form. In a particularlypreferred embodiment of such a method for modifying a continuouswaveform, the invention provides for the non-biological CPG to modifythe wave form. In another embodiment that is particularly preferred, theinvention provides for the the rule-application to be the application ofadaptive ring rules.

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The foregoing and other objects, aspects and advantages will bebetter understood from the following detailed description of thepreferred embodiments of the invention with reference to the drawings,in which:

[0034]FIG. 1 depicts the layout of an example of a CPG chip according tothe invention.

[0035]FIG. 2 is a schematic of an example of an integrate-and-firemotoneuron and synapse according to the invention.

[0036]FIG. 3 is an example of a circuit diagram which is according tothe invention and adaptively controls dynamics of a limb, by a neuralnon-biological CPG with learning capabilities.

[0037]FIG. 4 is a hip, knee and foot-contact phase diagram for arepresentative embodiment according to the invention.

[0038]FIG. 5 is a series of graphs, for a hip, knee, and foot, showingthe effect of lesioning sensory feedback when the invention is used.

[0039] FIGS. 6(a) and 6(b) are plots of hip position versus time for arobot according to the present invention.

[0040]FIG. 7(a) is a flow chart showing an exemplary relationship of aCPG chip according to the invention and sensory input. FIG. 7(b) is aflow chart showing a simple example of processing that occurs forinformation collected by a sensor according to the invention.

[0041]FIG. 8 is a cross-section view showing use of a CPG chip accordingto the invention in a human patient with a damaged spinal cord.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

[0042] The invention provides a non-biological CPG-based system which isadaptive to the environment in which it is used. To be adaptive, the CPGsystem receives sensory feedback from the environment and acts based onthe received sensory feedback. In one preferred embodiment, theinvention provides a CPG-based system that controls one or moremechanical limbs. In another preferred embodiment, the invention uses aCPG-based system for controlling rhythmic movement in a biologicalsystem, such as one or more biological limbs or structures. Walking,swimming, flying, hopping and breathing machines, by way of non-limitingexample, may be made using the invention. In the non-biologicalCPG-based system, a non-biological CPG generates commands that are afunction of sensory feedback.

[0043] “Sensory feedback” as mentioned in the present invention refersto any sensory information that is, or is processible into informationthat is, recognizable by a non-biological CPG. Sensory feedback may befrom a mechanical source (such as a mechanical limb, camera or otherartificial vision, artificial audition, artificial muscle sensors, etc.)or a biological system (such as neural signals from muscles, neuralsignals from brain regions, etc.). In a preferred embodiment of thepresent invention, sensory feedback from multiple sources is provided toa non-biological CPG.

[0044] For obtaining the sensory feedback from a mechanical source, oneor more sensors may be provided for collecting sensory feedback. Thesensor may receive the sensory feedback from any sensing modality. Asensor for use in the invention is not particularly limited and maybeartificial vision, artificial audition, artificial muscle sensors, orsensors for measuring any natural or environmental condition (such asweather, air quality or content (e.g., acidity), presence of chemicals,fire, temperature, pressure, lighting conditions, gunfire, microwaves,optical information, etc.), contact sensors, etc.

[0045] When a sensor is used, the positioning of the sensor is notparticularly limited with regard to the non-biological CPG, and thesensor and the CPG may be disposed in any manner in which they are incommunication with each other, directly or indirectly. The manner inwhich communication between a sensor and a non-biological CPG may beestablished, directly or indirectly, is not particularly limited, andthe sensor and non-biological CPG may be connected electrically such asby being wired to each other. In an example of a CPG chip according tothe invention shown in FIG. 1, all the components are individuallyaccessible such that they can be connected with off-chip wiring torealize any desired circuit. Neural CPG circuits can be integrated withcompletely hardwired or programmable circuits. The sensor and the CPGchip may be connected non-electrically, such as optically, by a wirelessconnection, by infra-red, chemically, or connected electrically andchemically.

[0046] The non-biological CPG and the sensor communicate in a languageunderstood by both, e.g., spike-coded, digital interface, analoginterface, etc. The language may be selected based on the non-biologicalCPG and the sensor being used. One skilled in the art is familiar withestablishing an interface according to the components to be put incommunication with each other, such as the non-biological CPG and thesensor.

[0047] In a preferred embodiment, a sensor may be used in relativelyclose proximity to the non-biological CPG, for providing informationabout the immediate environment of a CPG-based system according to theinvention, or of a patient in which a CPG-based system is operating.

[0048] However, it will be appreciated that in some applications it maybe advantageous to operate with a substantial distance between a sensorand a non-biological CPG. For example, a sensor may be placed on or inan object or person who may need rescuing, to be used, if needed, incooperation with a moving rescuer device comprising a non-biologicalCPG.

[0049] A non-biological CPG for use in the present invention is at leastone circuit that is pattern-generating and further is configured togenerate commands (such as self-adapting) as a function of sensoryfeedback. The pattern-generating of the non-biological CPG in a mostpreferred example may be provision of continuous waveforms, with thewaveforms being a function of one or more waveform parameters. Awaveform parameter may be any parameter that accomplishes an adjustmentassociated with a predetermined feature of movement, and may depend onthe movement. For example, in the case of two-legged movement, waveformparameters may include “center of stride”, “left/right” adjustment,“length of stride”, “frequency of stride”, “height”, etc. Those skilledin the art are familiar with generally selecting a wave-form for anon-biological CPG to match and thus provide a particular desiredmotion, such as selecting a waveform to provide a running motion with acertain center, length and frequency of stride. In the presentinvention, it is preferred that the non-biological CPG be configured toprovide a variety of continuous waveforms and to easily self-reset fromone waveform being generated to another waveform.

[0050] Preferably, a non-biological CPG for use in the invention is oneconfigured with safeguarding to only permit generation of waveformswithin the capabilities and performance specifications of the system inwhich it is used, such as not permitting hyper-extension, not generatinga wave-form that would overstretch or otherwise harm a patient in whichit is implanted, etc. On the other hand, preferably a non-biological CPGfor use in the invention within such a safeguarded range has a richarray of waveforms, to provide fine movement details.

[0051] To be suitable for use in the present invention, thenon-biological CPG is configured to generate commands as a function ofsensory feedback. For example, as seen with reference to FIGS. 7(a) and8, a CPG chip 1 which is a preferred embodiment of the invention may beused in a biological system such as a human patient. The CPG chip 1 mayreceive sensory feedback from neural signals from muscles 6, artificialvision 7, artificial audition 8, artificial muscle sensors 9, and neuralsignals from brain regions 10. The CPG chip 1 may issue commands 100 tomuscles and limbs 11. A dotted line shows direct and indirect influence110 of the sensory feedback.

[0052] When a CPG chip is used in a biological system, such as shown inFIGS. 7A and 8 by way of example, an interface between the CPG chip andthe biological system is provided. The interface is one that can providesensory feedback from the biological system to the CPG chip in the formof electrical signals. Measurement of electrical activity in neurons isknown in neurophysiology and such measurement techniques may be used inthe invention. The activity of neurons can be sensed using electronicprobes that measure the electrical discharge of the cells. These signalsare usually small, and may be amplified so that they can be provided tothe CPG chip as voltage spikes. The interface with the biological systemmay be any interface capable of providing voltage spikes to the CPG,such as chronically implanted micro electrodes that may be used tomeasure the electrical activity of neurons in the muscles, spine orbrain. The microprobes may be connected, with thin wires, to apre-amplifier chip that magnifies the signals from millivolts to volts.The magnified signals may be presented to the CPG chip as pulses to thesynapses.

[0053] The non-biological CPG of the present invention comprises anybiologically plausible circuit or circuits for controlling motorsystems. The definition of a non-biological CPG in terms of biologicalplausibility for controlling motor systems is not intended to limit anon-biological CPG according to the invention to motor systemsapplications.

[0054] An example of a non-biological CPG for use in the invention is amemory device combined with a system for manipulating neural phasicrelationships, such as a non-biological CPG that maintains a continuouswave-form and self-adapts the waveform based on sensory feedback.

[0055] As has been said, the non-biological CPG for use in the presentinvention is one that generates commands as a function of sensoryfeedback. In a preferred embodiment of the present invention, togenerate such commands, the sensory feedback which consists ofelectrical signals is evaluated to determine what, if any, effect thesensory feedback is to have on the wave form being provided. Thenon-biological CPG may be programed to recognize one or more sensorytriggers in the sensory feedback, and for each particular sensorytrigger found, to respond according to a predetermined rule. For suchevaluation of the sensory feedback, programming for cyclic readjustmentof signals, such as ARR, may be applied. ARR programming preferably isused for such evaluation of the sensory feedback. Sufficient memoryand/or data storage are provided to support evaluation of the sensoryfeedback.

[0056] By way of a non-limiting example, a non-biological CPG accordingto the present invention may be programmed to recognize in sensoryfeedback from a visual sensor a certain pattern as a hole, to treat thehole as a certain sensory trigger, and therefore to adjust the waveformin progress according to a certain pre-determined rule or set ofpre-determined rules.

[0057] Methods of evaluating and characterizing sensory feedback knownto those skilled in the art may be used in the present invention, suchas known methods of characterizing data from an artificial vision systemsuch as a camera, known methods of characterizing data from anartificial audition system, etc.

[0058] A non-limiting example of a CPG-based system according to theinvention is as follows, discussed with reference to FIGS. 7(a), 7(b)and 8. When the system is started, the CPG chip begins running aninitial continuous waveform. A sensor (such as artificial vision 7 inFIGS. 7(a) and 8 ) collects sensory information and, directly orindirectly (such as after processing to be readable by the CPG chip)sends sensory feedback to the CPG chip. The sensory feedback may be sentfrom the sensor to the CPG chip directly such as by a circuit. Sensoryfeedback information is transmitted for receipt by the CPG chip in theform of electrical signals.

[0059] As shown on FIG. 7(b), in a preferred embodiment of theinvention, a CPG chip receives sensory feedback from one or more sensors60. The CPG chip receives 70 the sensory feedback in the form ofelectrical signals, and then applies ARR 80 relating to predeterminedsensory triggers to the sensory feedback. Based on sensory triggers inthe sensory feedback, the CPG chip determines 90 whether to modify ormaintain the continuous waveform that is in progress. If the CPG chipdetermines to maintain 91 the waveform in progress, no change to anywaveform parameter is commanded. If the CPG chip determines to modify100 the waveform in progress, the CPG chip alters one or more waveformparameters, such as, in this example, “forward displacement”, “center ofstride”, “left/right”, “length of stride”, “frequency of stride”,“height”, etc.

[0060] Most preferably, the CPG applies ARR to the sensory feedback inas short a time as possible. For example, if a sensor in a walkingmachine provides sensory feedback indicating a hole, desirably suchsensory feedback is processed and acted on before the walking machinetravels into the hole.

[0061] With reference to FIG. 7(b), information resulting frommodification by the CPG 100 of 1 or more waveform parameters is sent tothe sensors 60. In FIG. 7(b), by way of example, sensory feedback isshown with regard to sensors 60, but it will be appreciated that thesensors 60 are only an example of a source of sensory feedback and mayinstead be a biological system, or a combination of sensors and abiological system.

[0062] As may be appreciated with reference to FIGS. 7(a) and 7(b),based on sensory feedback, a CPG-based system according to the inventionself-adapts. The self-adapting is not particularly limited, and maycomprise any command (such as a waveform phase adjustment or otheraction that the CPG undertakes) that is a function of sensory feedback.In a preferred example, the self-adapting comprises a system for phaseadjustment of the CPG based on a sensory trigger in or derived fromsensory feedback, or a system for controlling firing frequency ofmotoneurons as a function of the sensory feedback or the sensorytrigger.

[0063] In a preferred embodiment of the invention, at least one memorydevice is used. Preferably, the memory device is one that controlsadaptation of output such as output parameters from the CPG. The memorydevice may be a short-term memory device. Preferably, a high level ofabstraction is used to more easily implement on-chip learning. Systemsbased on numerous inter-related parameters are avoided because in suchsystems it is not apparent how learning at the level of behavior can becoupled to low level parameter changes. The memory device may be adynamic analog memory, or a digital memory device.

[0064] A CPG-based system according to the invention is not particularlylimited in its form, and may be in the form of one or more chips, arobot, a movement machine (such as a walking, running, swimming, flyingor breathing machine), a biological system etc.

[0065] An example of a preferred embodiment of the invention is a CPGchip 1 as shown in FIGS. 1, 7(a) and 8. As seen with reference to FIG.1, the CPG chip 1 includes phase controller 2 and pulse integrator 3.Burst duration neurons 4 and integrate-and-fire spiking neurons 5 areprovided on CPG chip 1. A schematic for an integrate-and-fire spikingneuron 5 is shown in FIG. 2.

[0066] In FIG. 2, on the right-hand side, hysteretic comparator 5 a,spike train 5 b and spike reset 5 c of neuron 5 are shown. The left-handside of FIG. 2 shows the schematic of the synapse 12 for neuron 5.Excitory spike 12 a and excitory weight 12 b, and inhibitory weight 12 cand inhibitory spike 12 d, are shown for synapse 12.

[0067] A chip used in a CPG-based system according to the invention mayinclude dynamic memories and phase modulators. A CPG chip according tothe present invention may be integrated with hardwired or programmablecircuits, or may be used in any other form. In FIG. 1, the CPG chip isshown with each component wired to pins to facilitate the prototyping ofoscillator circuits.

[0068] In a preferred embodiment of the invention, the CPG-based systemis a non-linear oscillator based on the CPG of a biological organism. Ina system comprising a non-linear oscillator, the system is non-linearand preferably includes a chip using non-linear elements, to provide acoupled system of non-linear elements, without linearizing the system.When a non-linear oscillator is used, because linearization is not used,instead principles from biological systems are used, which can beimplemented easily with low-power integrated circuits, to provide acompact system.

[0069] The system may include electronic analogues of biologicalneurons, synapses and time-constraints. FIG. 2 depicts some examples ofsuch electronic analogues.

[0070] The CPG-based system of the invention maybe a distributed systemof at least two non-linear oscillators, of which FIG. 3 is an example.The circuit of FIG. 3 adaptively controls dynamics of a limb, by aneural non-biological CPG with learning capabilities. Other distributedsystems of at least two non-linear oscillators may be used in theinvention. Such a distributed system may include at least one neuronphasically coupled to a neuron or a sensory input. Preferably, thedistributed system includes two or more neurons phasically coupled toeach other, to another neuron, or to a sensory input.

[0071] The phasic coupling used in the invention may be in-phase, 180degrees out of phase, or any amount out-of-phase. The phasic couplingmay be selected based on desired end-use application such as aparticular rhythmic movement. When an integrate-and-fire spikingmotoneuron is used, a preferred integrate-and-fire spiking motoneuron isone driven by phasically coupled neurons. The phasic coupling featuremay be provided as a phase control circuit, such as phase controller 2in FIG. 1. Phase control circuits and phasic coupling are known to thoseskilled in the art.

[0072] The inventive CPG-based system may include one or more mechanicalor biological limbs, examples of which are seen in FIGS. 7(a) and 8. Theinventive CPG-based system is not limited to limbs, and in the case ofbiological systems, may include any biological system for rhythmicmovement, in which case FIGS. 7(a) and 8 still are relevant, butreplacing reference to a limb with that of an organ or other componentof the body.

[0073] The CPG-based system according to the invention is especiallysuited for use in a biological system, such as the human body or ananimal. In such an application, the CPG-based system may include one ormore muscles, biological neurons, etc.

[0074] Above the invention has been discussed with regard to FIG. 1,showing a single CPG chip. It will be appreciated that FIG. 1 is anexample and that the invention may be practiced using two or more chips.When multiple chips are used for constructing a CPG-based systemaccording to the invention, those chips may be electrically connected asis known to those skilled in the art.

[0075] The invention also may be practiced by constructing a robot, suchas a robot comprising one or more CPG chips according to FIG. 1. Formaking a robot according to the invention, a CPG-based system thatmimics a biological CPG (such as a chip like that of FIG. 1) may beelectrically connected with one or more sensors, and further may beconnected with one or more memory devices. The memory device preferablyis programmed with a set of adaptive ring rules relating topredetermined triggers that may be found in the sensory feedback fromthe particular sensors that are being used.

[0076] In another preferred embodiment, the invention provides a methodfor controlling a mechanical limb or biological system for rhythmicmovement, by (A) measuring sensory feedback; (B) processing the measuredsensory feedback to obtain data for at least one designated parameter;and (C) via a CPG-based system, applying a set of rules to the obtaineddata to generate at least one signal for commanding the limb orbiological system for rhythmic movement. The processing may be to obtaindata for a plurality of designated parameters, or, in a most simpleexample, to obtain data for one parameter. The CPG-based system used maybe any system comprising a circuit that mimics a biological CPG. Such aninventive method preferably includes a further step of, via theCPG-based system, applying the generated signal to command the limb orbiological system for rhythmic movement. For practicing such a method,preferably the CPG-based system comprises a circuit comprising at leasttwo coupled non-linear oscillators.

[0077] In another preferred embodiment, the invention may be used toconstruct an autonomous movement device for providing rhythmic control.Such a device is constructed starting with a non-biological CPG thatgenerates rhythmic control commands that are a function of sensoryfeedback, to which is added one or more movement components, such asone, two or more mechanical limbs, which may be a leg, arm, wing orappendage for swimming, or other limb. Examples of such a movementdevice maybe a running device, a flying device, a hopping device, ajumping device, a walker, a breathing controller or a pacemaker.

[0078] It will be appreciated that the uses of the present invention arenot particularly limited, and examples may be biological and medicalapplications, movement and transportation applications, rescueapplications, space exploration, toys, etc. However, such examples ofuses of the present invention are not intended to be limiting.

[0079] As an example of a biological or medical application, the presentinvention in a particularly preferred embodiment may be used in treatingpatients with spinal total or partial spinal damage, such as byproviding a spinal neural stimulator for paraplegics with a transectedor crushed spinal cord. A chip according to the invention may be used tostimulate nerve cells that are responsible for walking. Such a chipoutputs signals that are compatible with biological neural circuits.

[0080] Another preferred use of the present invention is to provide achip that may control a leg vehicle (such as that of Iguana Robotics,Inc., PowerBoots technology) that assists the normal process of running,such that individuals can run faster, longer, jump higher while carryingmore weight. Such a leg vehicle has applications in both civilian andmilitary arenas. The adaptive properties of a control system accordingto the present invention allows a leg vehicle such as PowerBoot to“learn” the individual dynamics of the user and continuously fine-tuneitself to optimize running efficiency, speed and power source lifetime.In addition, using the present invention in a leg vehicle may decreasethe stress placed on the user by allowing the user to also modify thebehavior of the controller through direct inputs.

[0081] It will be appreciated that uses of the invention in biologicalor medical applications are not necessarily limited to those in which abiological CPG was or is operating (such as for locomotion control) butalso may be extended to uses in which a biological CPG may not have beeninvolved, such as timed-release and other drug delivery (such as in thecontext of stimulating nerves). For example, a non-biological CPG systemaccording to the invention may be implanted in the brain or spinal cordfor accomplishing drug delivery. Also, the invention may be used inneuro-stimulation applications, for controlling epilepsy, and forchemical-sensing in a patient.

[0082] Another preferred embodiment of the invention uses anon-biological CPG chip as a control system for a robot, such as walkingrobot. In a particularly preferred embodiment, the robot can navigaterough terrain. For navigating such terrain, a multi-pedal walkingmachine may be used, with a controlling comprising a non-biological CPGchip that coordinates the limbs and adapt their behaviors based on theenvironment. With visual inputs, the robots can use adaptive CPG chipsto run and hurdle obstacles at high speeds.

[0083] The present invention in another preferred embodiment is used inmedical and biological applications, such as an implantable,neurologically compatible neural surrogate for a paralyzed individual.Such a neural surrogate according to the invention has low powerconsumption and a biologically based nature and may be used to regulatebreathing, heart beat and other rhythmic movements. An implantableneural surrogate according to the invention also may be provided toadapt itself to optimize its own efficiency and that of the biologicalsystems it controls.

[0084] The invention also may be used in miniature systems to modulaterepetitive cyclical movements based on sensory feedback, such asminiature walking, running, flapping, flying and swimming machines.Ultra-small, adaptable control systems are preferred for such robots,for various reasons (such as providing an obtrusive device or to fitinto a limited space). The present invention can provide such controlsystems. Miniature robots and robotics systems may be used forreconnaissance, and search missions (such as in a fallen building afteran earthquake), and will benefit from a compact control system accordingto the present invention. Also, miniature embodiments of the presentinvention may be used in surgical applications, such as a catheterprovided with a miniature CPG chip controlling how the catheter contactsand wiggles as it “swims” in blood.

[0085] In other embodiments, the invention may be used in toyapplications, such as a toy animal comprising a controller according tothe invention. Because the invention can provide a controller that isrelatively small and low power, the controller can be mounted directlyon toy robot limbs to be controlled. In addition, its low power naturemeans that it will not drain the batteries quickly. The adaptive aspectmeans that the toy robot animal (such as a “Tabby” cat) can change itsgait based on the obstacles and type of environment in which it iswalking, without using any CPU, using controls that are biologicallyinspired, and using a distributed network of autonomous controllers thatare coupled through the dynamics of the toy robot and the properties ofthe environment.

EXAMPLE NO. 1

[0086] A robot comprising a biomorphic leg was constructed using aneuromorphic chip on which CPGs were modeled as distributed systems ofnon-linear oscillators. To provide basic coordination in a leg, twoneurons were phasically coupled together to achieve oscillations. Theywere coupled together to be alternatively active, with the alternatingactivity as the basic coordination that drove the hip of the robot. Aphase control circuit governed the phase difference between the neurons.The oscillator neurons drove two integrate-and-fire spiking motoneurons,which drove an actuator. (The spiking neuron could also drive biologicalmuscle or it could also be used to drive a pneumatic cylinder, aMcKibben actuator or biomuscle directly).

[0087] The robot used servomotors to provide electrical power. To becompatible with this technology, low-pass filters 14 were applied to thespiking neurons and the resulting smooth graded velocity signal wasintegrated.

[0088] The circuit was used in autonomous operation and with sensoryfeedback from stretch receptors used to adjust the CPG. Properties ofthe constructed biomorphic leg were demonstrated. The biomorphic limband its control circuit produced stable rhythmic motion, and alsocompensated for intentional biases in the chip as well as mechanicalcomplexity of an active hip and passive knee.

[0089] As basic components of the robot, neurons and a CPG chip wereused with a robotic leg.

[0090] Neurons

[0091] The neuron used was an integrate-and-fire model. A capacitor,representing the membrane capacitance of biological neurons, integratedimpinging charge. As seen with reference to FIG. 2, the capacitor wasset that when the “membrane-potential” exceeds the threshold of ahysteretic comparator 5 a, the neuron output is high. In this circuit,this logic high triggers a strong discharge current that adjusts themembrane potential to below the threshold of the comparator, thuscausing the neuron output to adjust. This circuit therefore emulated theslow phase and fast phase dynamics of real neurons, with the processthen starting anew. FIG. 2 shows a schematic of the neuron circuit usedin the robot of Example 1.

[0092] The neurons used in Example 1 were those that carry activationinformation in the frequency of spikes. The rate at which the membranepotential charges up controls the firing frequency of the neuron. Thisrate is given by the sum of the total charge flowing in and out of themembrane capacitance. The strength of the reset current sourcedetermines the width of each neural spike. The discharge current isusually set to a large value so that each spike is narrow and is notinfluenced by the charge injected onto the membrane capacitor.Typically, the neuron is set to fire at a nominal rate at rest, withadditional input increasing or decreasing the firing rate, and withshunting inhibition that can also silence the neuron.

[0093] The following equation (1) gives the dynamic equation for theneuron in Example 1. $\begin{matrix}\begin{matrix}{{C_{i}^{mem}\frac{V_{i}^{mem}}{t}} = {I_{spon} - {S_{i}I_{dis}} + {\sum\limits_{j}{S_{j}I_{j}^{+}}} - {\sum\limits_{j}{S_{j}I_{j}^{-}}}}} & (a) \\{S_{i} = \left\{ \begin{matrix}1 & {if} & {V_{i}^{mem} > V_{T}^{+}} \\0 & {if} & {V_{i}^{mem} < V_{T}^{-}}\end{matrix} \right.} & (b)\end{matrix} & (1)\end{matrix}$

[0094] With reference to equation (1), there are three input voltages:(1) a feedback input from a hysteretic comparator (S_(i)), (2)Excitatory inputs from other neurons (S_(i)) and (3) Inhibitory Inputsfrom other neurons ({overscore (S)}_(i)). These inputs are weighted bycurrent sources. These current sources are denoted I_(dis), I_(i) and{overscore (I)}_(i) respectively. In addition, a constant currentinjection sets a spontaneous spike rate of the neuron. As noted above,I_(dis) sets the spike duration. Finally, the term V_(T) ⁺ and V_(T) ⁻set the thresholds for the hysteretic comparator respectively.

[0095] The spike trains impinging on a neuron activate switches thatallow charge quanta to flow into or off the membrane capacitor. Theamount of charge transferred per spike is the synaptic weight and iscontrolled by an applied voltage that regulates the current sources.Modulation of this voltage allows the adaptation of the neural firingrate and is used during learning. The left-hand side of FIG. 2 shows theschematic of the synapse 12, while equation (1) above shows how theneuron is affected by the synaptic weight.

[0096] In addition to spiking neurons, neurons with graded response alsowere used in making the robot of Example 1. The graded-response neuronswere essentially the same as the spiking neuron except for replacing thehysteretic comparator with a linear amplifier stage and not usingfeedback voltage..

[0097] Oscillators on the CPG Chip

[0098] The neural circuits for creating the CPG were constructed usingcross-coupled square-wave oscillators, with the output of theseoscillators driving the bursting motoneurons described above. Amaster-slave configuration of the neurons was used, to allowconstruction of an oscillator with a constant phase relationship. Bysetting the excitatory and inhibitory weights to equal values, asquare-wave with a duty-cycle of 50% was obtained. The phaserelationship between the two sides was subject to being varied. Thefrequency of oscillation was set by the magnitude of the weights. Thisasymmetrically cross-coupled oscillator served as the basic CPG unit,with the oscillator subject to being modified according to theapplication so that if a different application was desired later, theoscillator could be reset. By injecting or removing charge from themembrane capacitors of the oscillator neurons, the properties of the CPGcould be altered.

[0099] To be able to produce more complex waveforms, a phase controllerwas included on the chip. This phase controller allows the phasedifference between oscillators to be set arbitrarily. For theexperiments described herein, a strict 180 degrees phase relationshipwas needed, hence, an inverted version of an oscillator was used, asshown in FIG. 3.

[0100] Neural Circuit on the CPG Chip

[0101] The complete neural circuit as used in making the robot ofExample 1 is shown in FIG. 3. The output of the basic oscillator unit 13was used to inhibit the firing of the spiking motoneuron. The oscillator13 was set so that when the oscillator output is high, the motoneuron isnot allowed to fire, which produces two streams of 180 degrees out ofphase spike trains. These trains could be low-pass filtered to get avoltage which could be interpreted as a motor velocity. Consequently,the oscillator controlled the length of the motor spike train, while thespike frequency indicated the motor velocity. The spike frequency wasregulated by a feedback loop. Spiking placed charges on the neuronmembrane capacitor seen in the lower part of FIG. 3. The integratedcharges were buffered and then used to down regulate spike frequency. Inthis way spike frequency was less sensitive to component variations.

[0102] Four neurons were provided as described above, in the form of acustom VLSI CPG chip occupying less than 0.4 square mm.

[0103] Robotic Leg

[0104] In assembling the self-adaptive robotic leg of Example 1, arobotic leg that was a small (10-cm height) two-joint mechanism wascombined with the above-mentioned CPG chip via components to interfacethe chip to the robotic leg, and a data collection facility. In therobot of Example 1, only the “hip” was driven with the “knee” beingcompletely passive and swinging freely, rotating on a low frictionball-bearing joint. A hard mechanical stop prevented the knee fromhyper-extending.

[0105] In Example 1, the neurons of the CPG chip were interfaced to aservomotor using a rudimentary muscle model. The muscle dynamics weresimulated as a low pass filter to smooth the output of the spikingneurons. This was followed by an integrator, implemented in software, toconvert the velocity signal to a position command needed by theservomotor. A bias (intended to be typical of uncompensated parametersin a chip) was intentionally introduced into the chip to cause anasymmetry in the backward and forward swing of the leg.

[0106] The robotic leg of Example 1 was provided with three sensors. TwoLVDT sensors monitored the position of the knee and hip joints. LVDTsensors were used because they introduced minimal friction and hadinfinite resolution. Additionally, the robot was provided with aminiature load-cell sensor that monitored ground forces. The units ofthe load cell are uncalibrated in all figures.

EXPERIMENTATION

[0107] Using the inventive robot of Example 1, two additional sensorymediated loops that adapt the oscillator and the motoneuron spiking wereadded, and testing relating to sensory adaptation and learning wasperformed. The inventive circuit used in the experimentation was oneconsuming less than one microwatt of power and occupying less than 0.4square millimeters of chip area.

[0108] Adaptation-based “Stretch Receptor”

[0109] As shown in FIG. 3, the oscillator neurons of the robot ofExample 1 could be stopped or started with direct inhibitory andexcitatory sensory inputs, respectively. When the inputs were receivedas strong inhibition, the membrane capacitor was shunted and dischargedcompletely. It remained in this state until the inhibition was released,then the normal dynamics of the oscillator continued from the inactivestate. On the other hand, if the sensory input was received as a strongexcitation, the oscillator was driven into an active state. When theexcitation was released, the oscillator continued from the active state.The charge-up or discharge of the membrane capacitor was influenced byany direct sensory input. For periodic sensory inputs, the oscillatoroutputs could be driven such that they phase locked to the inputs. Thusthe oscillator was entrained to the dynamics of the system undercontrol.

[0110] This property of the oscillator being entrained to the dynamicsof the system under control was used to mimic the effect of the stretchreflex in animals. When the leg of an animal is moved to an extremeposition, a special sensor called a stretch receptor sends a signal tothe animal's CPG causing a phase adjustment. This biological phaseadjustment response is mimicked in the circuit of Example 1. Referringto FIG. 3, the biomorphic leg of Example 1 may reach an extreme positionwhile still being driven by the oscillator. In this case, virtualposition sensors 16, which mimic stretch receptors, send a signal toResetA 15 a or ResetB 15 b to cause an adjustment of the oscillatorcircuit as appropriate to cause a hip joint velocity reversal.

[0111] Spike Frequency Adaptation

[0112] To provide learning, the chip included a short-term (on the orderof seconds) analog memory to store a learned weight. This architecturefavors a continuous leaning rule. Spikes from the motoneurons were usedto increase or decrease a voltage on a capacitor; the voltage was usedto set the connection weight of another neuron. In the absence of thetraining inputs, the stored weights decay at approximately 0.1V/s. FIG.3 shows a schematic for adapting the spiking frequency of themotoneurons based on the swing amplitude of the limb.

[0113] In FIG. 3, the limb was driven back and forth with a velocitysignal that was obtained by low-pass filtering the activity of themotoneurons. Because the CPG oscillator fixed the duration of the spiketrain, changing the spiking frequency of the motoneuron altered theamplitude of the velocity signals, which in turn varied the swingamplitude of the limb. If the amplitude of swing did not reach themaximum positions, the motoneuron spike rate was increased. An increasein spike rate was kept bounded by negative feedback to the learningcircuit. When the swing amplitude reached maximum, the positive input tothe learning circuit was reduced, thus allowing the spiking rate tosettle to a constant value. The continuous negative feedback of thespike rate and the input from the position detectors maintained thelearned spiking rate. The duration of the burst component of the spiketrain was further controlled by feeding the position signals directly tothe CPG oscillators to reverse the trajectory of motion at the endpoints. This allowed very asymmetric forward and backward velocitysignals to be adaptively re-centered.

[0114] Set-up

[0115] The small robotic leg of Example 1 was used for the experimentalset-up. The output of the hip LVDT was sampled digitally. The signal wasinterval coded. Two intervals were selected as representing the extremesof movement of the hip (called “virtual position sensor” in FIG. 3).When these extremes were reached, the corresponding interval was active.This interval then sent a signal to the CPG chip causing an appropriateadjustment.

[0116] An oscillator frequency was selected by hand to be approximately2-3 Hz. This frequency would excite the mechanical structure and causethe leg to “run” a rotating drum. In practice the leg was not highlysensitive to this excitation frequency but no effort was made toquantify this sensitivity.

[0117] Experiment 1: Running with a Passive Knee

[0118] With Example 1 in the above experimental setup, the CPG circuitwas set to drive the actuator in the hip joint. The knee joint waspassive and rotated with very little friction. The assembly wassuspended above a rotating drum. The CPG circuit was started, and datawas collected for three sensors, including foot pressure, knee and hip.“Stretch receptor” sensory feedback from the hip was used as feedback tothe CPG.

[0119] Running with the passive knee included a notable result that inthe system of Example 1 according to the present invention, the kneejoint adapted the correct dynamics to enable running. As the upper limbswung forward, the lower limb rotated so that the foot came off theground. When the upper limb was suddenly accelerated backward, themomentum in the lower limb forced the knee to lock in place. At just thecorrect moment, the foot contacted the ground and the subsequent loadingkept the knee joint locked in place. As the foot traveled backward iteventually began to unload. Stored energy in the elastic foot caused itto “kick up” and smartly snap off the ground, an effect most noticeableat higher velocities.

[0120]FIG. 4 shows a phase plot of the knee, foot and hip position andfoot contact for the robot of Example b 1 when Experiment 1 wasperformed. In FIG. 4, most of the trajectory is in a tight bundle, whilethe outlying trajectories represent perturbations. The bulk of thetrajectory describes a tight ‘spinning top’ shaped trajectory while thefew outlying trajectories are caused by disturbances. After adisturbance the trajectory quickly returns to its nominal orbit, whichreflects that the system was stable.

[0121] Experiment 2: Sensory Feedback Lesioning

[0122] Experiment 1 was repeated except for lesioning (turning off)sensor feedback periodically. Data was collected as in Experiment 1.

[0123]FIG. 5 shows the effect of lesioning sensory feedback on theposition of the hip and knee joints as well as the tactile input to thefoot. After lesioning the leg drifted backward significantly due to abias built into the chip. When the sensory input was restored, the legreturned to a stable gait. When the feedback was lesioned (Time 11-19seconds and 31-42 seconds), the hip drove backward significantly. As itdid the foot began to lose contact with the surface and the knee stoppedmoving. When the lesion was reversed at 19 and 42 seconds, theregularity of the gait was restored.

[0124] FIGS. 6(A) and (B) show the effect of perturbations on gait withintact and lesioned sensory feedback. In FIG. 6(A), five sequentialtrajectories (numbered) in intact and lesioned conditions arerepresented as ranging between black and light gray. A perturbation at 2in the intact case lead initially to worse trajectories (3 and 4), butquickly stabilized to the nominal orbit (5). In the lesioned case, chipbias caused a perturbation at 2 from which the gait could not recover;the hip was forced backward (3,4, and 5). In FIG. 6(B), the same tentrajectories shown in FIG. 6(A) are presented as hip positions throughtime, with various knee positions numbered. Intact sensory feedbackpermitted recovery while lesioning caused drift of both the hip andknee.

[0125] The experimentation provided the following information about gaitstability for the robot of Example 1. Perturbations to the leg causedmomentary disturbances. As seen in FIG. 4, several of the trajectoriesare clear “outlyers” to the typical orbit, and resulted fromenvironmental disturbances. It was found that sensory feedback couldcompensate for both the bias of the chip and environmentalperturbations. FIGS. 6(A) and (B) show restoration to a nominal orbitafter perturbation in intact and lesioned cases. In the intact case,aperturbation at cycle ‘2’ lead to outlying trajectories, but thetrajectory was quickly restored to the nominal orbit. In the lesionedcase, removal of sensory feedback caused the chip bias to destroy thetrajectory of the leg. The gait quickly deteriorated.

[0126] Thus, the present inventors have provided what they believe to bethe first experimental results of an adaptive VLSI neural chipcontrolling a robotic leg. Using sensory feedback, the circuit adaptedthe gait of the leg to compensate both for chip bias and environmentalperturbations. This work represents the first experimental results knownto the present inventors of an adaptive VLSI neural chip controlling arobot leg. The experimentation set forth herein establishes a successfulworking hardware implementation of a CPG-based model according to theinvention. The data of FIGS. 4, 5 and 6 establish that a VLSI chipaccording to the invention having only 4 neurons and occupying less than0.4 square nm controlled a leg running on a treadmill.

[0127] The data also reflect success in providing running as a dynamicprocess, for the under-actuated robotic leg of Example 1. In the resultspresented here, the energy injected into the hip was sufficient toexcite an orbital trajectory of the knee as well. The hip, knee, andfoot sensor orbit appear remarkably stable when the CPG circuit wasstabilized using sensory feedback. The data of FIGS. 4, 5 and 6 reflectthat control of a running leg using a VLSI CPG chip. The data furtherreflect successful application of a neuromorphic approach to build acomplete artificial nervous system to control a robot. The functioningof the robot of Example 1 confirmed the implementation of an adaptiveCPG model in a compact analog VLSI circuit. The experimentation confirmsthat the circuit in use has adaptive properties that allow it to tuneits behavior based on sensory feedback. The adaptive CPG chip accordingto the present invention is thought to be the first functioning adaptiveCPG chip. The results of the experimentation suggest that inexpensive,low power and compact controllers for walking, flying and swimmingmachines and other movement machines may be constructed using thepresent invention.

[0128] Also, the experimentation confirms that the invention provides afunctioning chip, based on principles of the locomotor-control circuitsin the nervous system, that mimics many of the features of a biologicalCPG. A circuit according to the invention was shown to control arobotics leg running on a circular treadmill. Furthermore, a circuitaccording to the invention was shown to use sensory feedback tostabilize the rhythmic movements of the leg The experimentation confirmsthat the invention may provide inexpensive circuits that are adaptable,controllable and able to generate complex, coordinated movements. Theexperimentation establishes that the present invention providesself-adaptation in a CPG-based system based on sensory input.

[0129] While the invention has been described in terms of its preferredembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims.

We claim:
 1. A central pattern generator-based system for controlling atleast one mechanical limb, comprising at least one mechanical limb; anda non-biological central pattern generator that generates commands forcontrolling the at least one mechanical limb wherein commands are afunction of sensory feedback.
 2. The central pattern generator-basedsystem of claim 1, including a system for phase adjustment of thecentral pattern generator based on a sensory trigger in or derived fromsensory feedback.
 3. The central pattern generator-based system of claim1, including: a system for phase adjustment of the central patterngenerator based on at least one sensory trigger in or derived fromsensory-feedback; and a system for controlling firing frequency ofmotoneurons as a function of the sensory feedback or the sensorytrigger.
 4. The central pattern generator-based system of claim 1,further including at least one memory device.
 5. The central patterngenerator-based system of claim 4, wherein the memory device controlsadaptation of output from the central pattern generator.
 6. The centralpattern generator-based system of claim 5, wherein the output includesintegrate-and-fire neurons.
 7. The central pattern generator-basedsystem of claim 1, wherein the system is at least one chip.
 8. Thecentral pattern generator-based system of claim 7, including at leastone chip containing electronic analogues of biological neurons, synapsesand time-constraints.
 9. The central pattern generator-based system ofclaim 7, including at least one chip that includes dynamic memories andphase modulators.
 10. The central pattern generator-based system ofclaim 1, wherein the system is a non-linear oscillator includingelectronic analogues of biological neurons, synapses andtime-constraints, dynamic memories and phase modulators.
 11. The centralpattern generator-based system of claim 7, wherein the system includesat least one chip in which components are integrated with hardwired orprogrammable circuits.
 12. The central pattern generator-based system ofclaim 1, wherein the central pattern generator is a distributed systemof at least two non-linear oscillators.
 13. The central patterngenerator-based system of claim 12, wherein the distributed systemincludes at least one neuron phasically coupled to a neuron or a sensoryinput.
 14. The central pattern generator-based system of claim 12,wherein the distributed system includes at least two neurons phasicallycoupled to each other, to another neuron, or to a sensory input.
 15. Thecentral pattern generator-based system of claim 14, wherein phasiccoupling is in-phase, 180 degrees out of phase, or any number of degreesout of phase.
 16. The central pattern generator-based system of claim14, wherein phasic coupling is based on rhythmic movement application.17. The central pattern generator-based system of claim 14, including aphase control circuit.
 18. The central pattern generator-based system ofclaim 14, including at least one integrate-and-fire spiking motoneurondriven by the phasically coupled neurons.
 19. The central patterngenerator-based system of claim 1, including at least one muscle. 20.The central pattern generator-based system of claim 1, wherein thesystem is a robot.
 21. The central pattern generator-based system ofclaim 7, wherein the system includes a central pattern generator chipand at least one biological neuron.
 22. The central patterngenerator-based system of claim 21, including multiple chips.
 23. Thecentral pattern generator-based system of claim 1, including at leastone sensor for collecting sensory feedback.
 24. The central patterngenerator system of claim 23, including a system for phase adjustment ofthe central pattern generator based on at least one sensory trigger inthe received sensory feedback.
 25. The central pattern generator-basedsystem of claim 1, wherein the sensory feedback is received from the atleast one mechanical limb.
 26. The central pattern generator-basedsystem of claim 1, wherein the sensory feedback is received from asensing modality.
 27. A central pattern generator-based system forcontrolling a biological system for rhythmic movement, comprising aninterface with a biological system that can provide sensory feedbackfrom said biological system; and a non-biological central patterngenerator that generates commands for controlling the biological systemwherein commands are a function of sensory feedback.
 28. The centralpattern generator-based system of claim 27, including a system for phaseadjustment of the central pattern generator based on a sensory triggerin or derived from sensory feedback.
 29. The central patterngenerator-based system of claim 27, including: a system for phaseadjustment of the central pattern generator based on at least onesensory trigger in or derived from sensory feedback; and a system forcontrolling firing frequency of motoneurons as a function of the sensoryfeedback or the sensory trigger.
 30. The central pattern generator-basedsystem of claim 27, further including at least one memory device. 31.The central pattern generator-based system of claim 30, wherein thememory device controls adaptation of output from the central patterngenerator.
 32. The central pattern generator-based system of claim 31,wherein the output includes integrate-and-fire neurons.
 33. The centralpattern generator-based system of claim 27, wherein the system is atleast one chip.
 34. The central pattern generator-based system of claim33, including at least one chip containing electronic analogues ofbiological neurons, synapses and time-constraints.
 35. The centralpattern generator-based system of claim 33, including at least one chipthat includes dynamic memories and phase modulators.
 36. The centralpattern generator-based system of claim 27, wherein the system is anon-linear oscillator including electronic analogues of biologicalneurons, synapses and time-constraints, dynamic memories and phasemodulators.
 37. The central pattern generator-based system of claim 33,wherein the system includes at least one chip in which components areintegrated with hardwired or programmable circuits.
 38. The centralpattern generator-based system of claim 27, wherein the central patterngenerator is a distributed system of at least two non-linearoscillators.
 39. The central pattern generator-based system of claim 38,wherein the distributed system includes at least one neuron phasicallycoupled to a neuron or a sensory input.
 40. The central patterngenerator-based system of claim 38, wherein the distributed systemincludes at least two neurons phasically coupled to each other, toanother neuron, or to a sensory input.
 41. The central patterngenerator-based system of claim 40, wherein phasic coupling is in-phase,180 degrees out of phase, or any number of degrees out of phase.
 42. Thecentral pattern generator-based system of claim 40, wherein phasiccoupling is based on rhythmic movement application.
 43. The centralpattern generator-based system of claim 40, including a phase controlcircuit.
 44. The central pattern generator-based system of claim 40,including at least one integrate-and-fire spiking motoneuron driven bythe phasically coupled neurons.
 45. The central pattern generator-basedsystem of claim 27, including at least one muscle.
 46. The centralpattern generator-based system of claim 33, wherein the system includesa central pattern generator chip and at least one biological neuron. 47.The central pattern generator-based system of claim 46, includingmultiple chips.
 48. The central pattern generator-based system of claim27, including at least one sensor for collecting sensory feedback. 49.The central pattern generator system of claim 48, including a system forphase adjustment of the central pattern generator based on at least onesensory trigger in the received sensory feedback.
 50. The centralpattern generator-based system of claim 27, wherein the sensory feedbackis received from the at least one biological limb.
 51. The centralpattern generator-based system of claim 27, wherein the sensory feedbackis received from a sensing modality.
 52. A method for controlling amechanical or biological system for rhythmic movement, comprising: (A)measuring sensory feedback to obtain measured sensory feedback; (B)processing the measured sensory feedback to obtain data for a pluralityof designated parameters; and (C) via a central pattern generator-basedsystem, applying a set of rules to the obtained data to generate atleast one signal for commanding the limb or biological system forrhythmic movement, wherein the central pattern generator-based systemcomprises a circuit that mimics a biological central pattern generator.53. The method of claim 52, including (D) via the central patterngenerator-based system, applying the generated signal to command thelimb or biological system for rhythmic movement.
 54. The method of claim52, wherein the central pattern generator system comprises a circuitcomprising at least two coupled non-linear oscillators.
 55. A roboticssystem comprising: (a) a central pattern generator-based system thatmimics a biological central pattern generator; and (b) at least onesensory device.
 56. The robotics system of claim 55, wherein the centralpattern generator-based system receives sensory input from the at leastone sensory device.
 57. An autonomous movement device for providingrhythmic control, wherein the autonomous device comprises: anon-biological central pattern generator that generates rhythmic controlcommands wherein commands are a function of sensory feedback.
 58. Theautonomous movement device of claim 57, including at least onemechanical limb.
 59. The autonomous device of claim 58 wherein the limbis a leg, arm, wing or appendage for swimming.
 60. The movement deviceof claim 58 including at least two limbs.
 61. The movement device ofclaim 57, wherein the device is a breathing controller.
 62. The movementdevice of claim 57, wherein the device is a pacemaker.
 63. The movementdevice of claim 57, wherein the device is a running device.
 64. Anon-biological central pattern generator comprising: a memory device;and a system for manipulating neural phasic relationships.
 65. A methodfor modifying a continuous waveform provided by a non-biological centralpattern generator, comprising the steps of: (A) provision of acontinuous waveform by a non-biological central pattern generator; (B)provision of sensory feedback to the non-biological central patterngenerator; (C) rule-application by the non-biological central patterngenerator to the sensory feedback; (D) based on the rule-application,determination by the non-biological central pattern generator to modifyor maintain the continuous wave form.
 66. The method of claim 65,wherein the non-biological central pattern generator modifies the waveform.
 67. The method of claim 65, wherein the rule-application is theapplication of adaptive ring rules.